A Synthesis of Climate Change Impacts on Stormwater Management Systems: Designing for Resiliency and Future Challenges
Bibliographic record
Abstract
Climate change is projected to alter rainfall patterns in many parts of the US and around the world, highlighting the importance of stormwater management systems within resiliency efforts. Stormwater systems typically are designed based on historical rainfall records with the assumption of climate stationarity. This assumption is no longer valid for many locations, leaving a gap in the knowledge about how to ensure that these systems will meet the desired level of service over their design life. Researchers and practitioners have begun exploring how to incorporate future climate scenarios into the design of stormwater systems to maintain the current level of function well into the future. Despite this, uncertainty remains about how to manage cloudburst events, the water quality implications of climate change, and how to incorporate uncertainty in climate model outputs into engineering designs. In the absence of unifying design criteria for incorporating climate change into infrastructure design, communities have begun to form their strategies, from updating intensity–duration–frequency curves to characterizing rainfall based solely on “recent” historical data. As the debate continues regarding how to best protect communities against uncertain future weather patterns, a set of critical considerations has emerged. There is a dire need to explicitly define what resiliency means for stormwater management systems under a climate change paradigm to allow for clear design criteria that incorporate uncertainty and can achieve favorable outcomes at the system scale. There also is ample opportunity to develop new approaches and technologies that allow communities to optimize their infrastructure in terms of water management and an array of other ecosystem services. Thus, despite the current and future challenges of climate change, opportunities exist to develop the next generation of stormwater management systems that serve as multifunctional community assets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".